We propose an algorithm for solving the surrogate dual of a mixed integer program. The algorithm uses a trust region method based on a piecewise affine model of the dual surrogate value function. A new and much more flexible way of updating bounds on the surrogate dual’s value is proposed, which numerical experiments prove to be advantageous. A proof of convergence is given and numerical tests ...

| Surrogate data techniques provide a statistical test for membership of certain classes of dynamical systems. Standard surrogate data techniques do not work well when applied to data that exhibits a strong periodic trend. In this paper we propose a new surrogate method that is able to mimic cyclic behaviour in time series and may be employed to test the null hypothesis of a periodic orbit with...

Evaluation of failure probability of a given system requires sampling of the system response and can be computationally expensive. Therefore it is desirable to construct an accurate surrogate model for the system response and subsequently to sample the surrogate model. In this paper we discuss the properties of this approach. We demonstrate that the straightforward sampling of a surrogate model...

The paper describes an evolutionary algorithm for the general nonlinear programming problem using a surrogate model. Surrogate models are used in optimization when model evaluation is expensive. Two surrogate models are implemented, one for the objective function and another for a penalty function based on the constraint violations. The proposed method uses a sequential technique for updating t...

This short chapter introduces the SURROGATE data type, which is useful when modeling time-varying objects. Surrogates are unique identifiers that can be compared for equality, but the values of which cannot be seen by the users. In this sense, a surrogate is “pure” identity and does not describe a property (i.e., it has no observable value). For this reason, a SURROGATE data type cannot be trea...

Mikhail A. Bragin • Peter B. Luh • Joseph H. Yan • Nanpeng Yu • Gary A. Stern Communicated by Fabián Flores-Bazàn Abstract Studies have shown that the surrogate subgradient method, to optimize non-smooth dual functions within the Lagrangian relaxation framework, can lead to significant computational improvements as compared to the subgradient method. The key idea is to obtain surrogate subgradi...

nonlinear knapsack problems (nkp) are the alternative formulation for the multiple-choice knapsack problems. a powerful approach for solving nkp is dynamic programming which may obtain the global op-timal solution even in the case of discrete solution space for these problems. despite the power of this solu-tion approach, it computationally performs very slowly when the solution space of the pr...

A special class of the knapsack problem is called the separable nonlinear knapsack problem. This problem has received considerable attention recently because of its numerous applications. Dynamic programming is one of the basic approaches for solving this problem. Unfortunately, the size of state-pace will dramatically increase and cause the dimensionality problem. In this paper, an efficient a...